About: Boosting algorithms for classification and regression, with many variations. Features include: Scalable and robust; Easily customizable loss functions; One-shot training for an entire regularization path; Continuous checkpointing; much more

• added ElasticNets as a regularization option
• fixed some segfaults, memory leaks, and out-of-range errors, which were creeping in in some corner cases
• added a couple of I/O optimizations

About: [FACTORIE](http://factorie.cs.umass.edu) is a toolkit for deployable probabilistic modeling, implemented as a software library in [Scala](http://scala-lang.org). It provides its users with a succinct language for creating [factor graphs](http://en.wikipedia.org/wiki/Factor_graph), estimating parameters and performing inference. It also has implementations of many machine learning tools and a full NLP pipeline.

Initial Announcement on mloss.org.

About: Very fast implementation of the chi-squared distance between histograms (or vectors with non-negative entries).

Removed bug in symmetric chi-square distance and updated python wrapper to python 2.5 compatiblity.

About: A K-means clustering implementation for command-line, Python, Matlab and C. This algorithm yields the very same solution as standard Kmeans, even after each iteration. However it uses some triangle [...]

Initial Announcement on mloss.org.

About: The Hidden Topic Markov Model

Initial Announcement on mloss.org.